克拉玛依市老年人认知减退、抑郁状态现状分析及影响因素的研究
A Study on the Current Status and Influencing Factors of Cognitive Decline and Depressive State among the Elderly in Karamay City
DOI: 10.12677/acm.2025.1572134, PDF,    科研立项经费支持
作者: 冯 微, 张 炜, 何 莉, 殷振江*:克拉玛依市中心医院神经内科,新疆 克拉玛依;李志雄:克拉玛依市人民医院精神心理科,新疆 克拉玛依;袁 君:克拉玛依市中心医院组织人事科,新疆 克拉玛依;李丽娇:新疆维吾尔自治区人民医院肾病科,新疆 乌鲁木齐
关键词: 老年人认知障碍抑郁状态影响因素Older People Cognitive Impairment Depressive State Influencing Factors
摘要: 目的:本研究旨在探究克拉玛依市65岁以上老年人认知减退及抑郁状态的现状,并剖析其影响因素,为社区开展老年人认知减退及抑郁状态的早期干预提供科学依据。方法:研究采用简单随机抽样与分层抽样相结合的方式,选取13,286名65岁以上老人作为调查对象,借助AD8、PHQ-9自评工具进行手机问卷调查。结果:13,286名调查对象中,认知功能下降组有3819人(占比28.745%),认知功能正常组9467人(占比71.255%),认知功能下降组年龄显著高于正常组(P < 0.01);可能或已处于抑郁状态组1954人(占比14.707%),无抑郁组11,332人(占比85.293%),抑郁组年龄显著高于正常组(P < 0.01)。认知功能下降组和正常组在年龄、性别、婚姻状况、居住方式、家庭月收入、既往史、家人健康状况、文化程度方面存在显著差异(均P < 0.05)。单因素Logistic回归分析表明,年龄、性别、婚姻状况、居住方式、家庭月收入、既往史、家人健康状况、文化程度是认知功能下降的风险因素;多因素Logistic回归分析显示,年龄、家庭月收入、既往史、家人健康状况、文化程度是认知功能下降的风险因素。可能或已处于抑郁状态组和正常组在年龄、性别、婚姻状况、家庭月收入、既往史、家人健康状况、文化程度方面差异显著(均P < 0.05)。单因素Logistic回归分析显示,上述因素是可能或已处于抑郁状态的风险因素;多因素Logistic回归分析表明,婚姻状况、家庭月收入、既往史、家人健康状况、文化程度是可能或已处于抑郁状态的风险因素。结论:克拉玛依市老年人认知功能障碍患病率为28.745%,独居、家庭月收入低、有既往慢性病史、家人健康状况差、文化程度低是老年人认知功能下降的高危因素;可能或已处于抑郁状态患病率为14.707%,性别、婚姻状况、家庭月收入低、既往有慢性病史、家人健康状况差、文化程度低是抑郁障碍的风险因素,需重点关注。
Abstract: Objective: This study aims to explore the current status of cognitive decline and depressive state among the elderly over 65 years old in Karamay City and analyze its influencing factors, so as to provide a scientific basis for the early intervention of cognitive decline and depressive state among the elderly in the community. Methods: The study adopted a combination of simple random sampling and stratified sampling to select 13,286 elderly people over 65 years old as the research objects, and used self-assessment tools such as AD8 and PHQ-9 for mobile phone questionnaires. Results: Among the 13,286 respondents, there were 3819 people in the cognitive function decline group (accounting for 28.745%), and 9467 people in the normal cognitive function group (accounting for 71.255%). The age of the cognitive function decline group was significantly higher than that of the normal group (P < 0.01). There were 1954 people in the possible or existing depressive state group (accounting for 14.707%), and 11,332 people in the non-depressive group (accounting for 85.293%). The age of the depressive group was significantly higher than that of the normal group (P < 0.01). There were significant differences between the cognitive function decline group and the normal group in terms of age, gender, marital status, living method, monthly family income, past medical history, family members’ health status, and educational level (all P < 0.05). Univariate Logistic regression analysis showed that age, gender, marital status, living method, monthly family income, past medical history, family members’ health status, and educational level were risk factors for cognitive function decline. Multivariate Logistic regression analysis showed that age, monthly family income, past medical history, family members’ health status, and educational level were risk factors for cognitive function decline. There were significant differences between the possible or existing depressive state group and the normal group in terms of age, gender, marital status, monthly family income, past medical history, family members’ health status, and educational level (all P < 0.05). Univariate Logistic regression analysis showed that the above-mentioned factors were risk factors for the possible or existing depressive state. Multivariate Logistic regression analysis showed that marital status, monthly family income, past medical history, family members’ health status, and educational level were risk factors for the possible or existing depressive state. Conclusion: The prevalence of cognitive impairment among the elderly in Karamay City is 28.745%. Living alone, low monthly family income, past chronic medical history, poor health status of family members, and low educational level are high-risk factors for cognitive function decline among the elderly. The prevalence of a possible or existing depressive state is 14.707%. Gender, marital status, low monthly family income, past chronic medical history, poor health status of family members, and low educational level are risk factors for depressive disorders, which require key attention.
文章引用:冯微, 李志雄, 张炜, 袁君, 何莉, 李丽娇, 殷振江. 克拉玛依市老年人认知减退、抑郁状态现状分析及影响因素的研究[J]. 临床医学进展, 2025, 15(7): 1350-1357. https://doi.org/10.12677/acm.2025.1572134

参考文献

[1] 杜鹏, 李龙. 新时代中国人口老龄化长期趋势预测[J]. 中国人民大学学报, 2021, 35(1): 96-109.
[2] 汪斌. 中国老年人口健康现状、变动趋势及其社会经济影响——基于“七普”数据的分析[J]. 云南民族大学学报(哲学社会科学版), 2022, 39(5): 68-75.
[3] 张玲, 徐勇, 聂宏伟. 2000-2010年中国老年人抑郁患病率的Meta分析[J]. 中国老年学杂志, 2011, 31(17): 3349-3352.
[4] 聂晓璐, 吕晓珍, 卓琳, 等. 2001-2015年中国轻度认知功能障碍患病率的Meta分析[J]. 中华精神科杂志, 2016, 49(5): 298-306.
[5] Herber, C.C.L.M., Lott-Sandkamp, L.L., Straub, E.R. and Tuschen-Caffier, B. (2024) The Role of Affective Control, Strategy Repertoire and Subjective Emotion Regulation Success in Developmental Internalising Psychopathology. Scientific Reports, 14, Article No. 21224. [Google Scholar] [CrossRef] [PubMed]
[6] Wu, X., Zhang, N., Chao, J., Liu, Y. and Zhang, B. (2024) Sex-Specific in the Association between Depressive Symptoms and Risk of Cognitive Impairment in Chinese Older Adults. Archives of Psychiatric Nursing, 52, 69-75. [Google Scholar] [CrossRef] [PubMed]
[7] Li, X., Wei, C., Hu, K., Sun, J., Gao, X. and Yang, J. (2024) Regional Differences in the Association of Healthy Aging with the Incidence of Falls: An Analysis Based on the China Health and Retirement Longitudinal Study from 2011 to 2020. Frontiers in Public Health, 12, Article 1416214. [Google Scholar] [CrossRef] [PubMed]
[8] Imarisio, A., Yahyavi, I., Gasparri, C., Hassan, A., Avenali, M., Di Maio, A., et al. (2024) Serum Dysregulation of Serine and Glycine Metabolism as Predictive Biomarker for Cognitive Decline in Frail Elderly Subjects. Translational Psychiatry, 14, Article No. 281. [Google Scholar] [CrossRef] [PubMed]
[9] Kim, A., Chu, S.H., Oh, S.S., Lee, E., Choi, J. and Kim, W.J. (2024) Subjective Cognitive Decline in Community-Dwelling Older Adults with Objectively Normal Cognition: Mediation by Depression and Instrumental Activities of Daily Living. Psychiatry Investigation, 21, 583-589. [Google Scholar] [CrossRef] [PubMed]
[10] Porricelli, D., Tecilla, M., Pucci, V., Di Rosa, E., Mondini, S. and Cappelletti, M. (2024) Cognitive Reserve Modulates Mental Health in Adulthood. Aging Clinical and Experimental Research, 36, Article No. 139. [Google Scholar] [CrossRef] [PubMed]
[11] Khan, H., Farhana, F., Mostafa, F., Rafiq, A., Nizia, E.W., Razzaq, R., et al. (2024) Comparative Study of Risk Factors Associated with Normal Cognition and Cognitive Impairment in Rural West Elderly Texans. Journal of Alzheimers Disease Reports, 8, 1133-1151. [Google Scholar] [CrossRef] [PubMed]
[12] Ojagbemi, A., Daley, S., Feeney, Y. and Gureje, O. (2024) The Care of Older People with Depression in Nigeria: Qualitative Exploration of the Experience of Lay Providers in Primary Care Settings. International Journal of Geriatric Psychiatry, 39, e6147. [Google Scholar] [CrossRef] [PubMed]
[13] Shakil, S., Ghosh, J., Singh, K. and Chaudhury, S.R. (2024) Comparative Analysis of Nutritional Status among Institutionalised and Community-Dwelling Elderly Women and Its Association with Mental Health Status and Cognitive Function. Journal of Family Medicine and Primary Care, 13, 3078-3083. [Google Scholar] [CrossRef] [PubMed]
[14] Ding, R., Ding, P., Tian, L., Kuang, X., Huang, B. and Lin, C. (2024) Associations between Sleep Duration, Depression Status, and Cognitive Function among Chinese Elderly: A Community-Based Study. Journal of Affective Disorders, 366, 273-282. [Google Scholar] [CrossRef] [PubMed]
[15] Didikoglu, A., Guler, E.S., Turk, H.K., Can, K., Erim, A.N., Payton, A., et al. (2024) Depression in Older Adults and Its Associations with Sleep and Synaptic Density. Journal of Affective Disorders, 366, 379-385. [Google Scholar] [CrossRef] [PubMed]
[16] Xue, S., Lu, A., Chen, W., Li, J., Ke, X. and An, Y. (2024) A Latent Profile Analysis and Network Analysis of Anxiety and Depression Symptoms in Chinese Widowed Elderly. Journal of Affective Disorders, 366, 172-180. [Google Scholar] [CrossRef] [PubMed]
[17] Szota, M., Rogowska, A.M., Kwaśnicka, A. and Chilicka-Hebel, K. (2024) The Indirect Effect of Future Anxiety on the Relationship between Self-Efficacy and Depression in a Convenience Sample of Adults: Revisiting Social Cognitive Theory. Journal of Clinical Medicine, 13, Article 4897. [Google Scholar] [CrossRef] [PubMed]
[18] Xiang, X., Turner, S., Ruiz-Sierra, S., Zheng, C., Ash, S., Kodkany, N., et al. (2024) Older Adults Experience with a Layperson-Supported Digital Mental Health Intervention for Depression: Qualitative Insights on Engagement. Clinical Gerontologist. [Google Scholar] [CrossRef] [PubMed]
[19] Dhawale, K.K. and Tidake, P. (2024) Cataract Surgery and Mental Health: A Comprehensive Review on Outcomes in the Elderly. Cureus, 16, e65469. [Google Scholar] [CrossRef] [PubMed]
[20] Götze, H., Friedrich, M., Taubenheim, S., Dietz, A., Lordick, F. and Mehnert, A. (2019) Depression and Anxiety in Long-Term Survivors 5 and 10 Years after Cancer Diagnosis. Supportive Care in Cancer, 28, 211-220. [Google Scholar] [CrossRef] [PubMed]
[21] Jia, F., Wang, J., Wei, N., Sun, D. and Cao, F. (2021) Depression, Cognitive Reserve Markers, and Dementia Risk in the General Population. Aging & Mental Health, 26, 2006-2013. [Google Scholar] [CrossRef] [PubMed]
[22] Banjongrewadee, M., Wongpakaran, N., Wongpakaran, T., Pipanmekaporn, T., Punjasawadwong, Y. and Mueankwan, S. (2020) The Role of Perceived Stress and Cognitive Function on the Relationship between Neuroticism and Depression among the Elderly: A Structural Equation Model Approach. BMC Psychiatry, 20, Article No. 25. [Google Scholar] [CrossRef] [PubMed]
[23] Huang, W., Zhu, W., Chen, H., Li, F., Huang, J., Zhou, Y., et al. (2022) Longitudinal Association between Depressive Symptoms and Cognitive Decline among Middle-Aged and Elderly Population. Journal of Affective Disorders, 303, 18-23. [Google Scholar] [CrossRef] [PubMed]
[24] Lin, L., Jing, X., Lv, S., Liang, J., Tian, L., Li, H., et al. (2020) Mobile Device Use and the Cognitive Function and Depressive Symptoms of Older Adults Living in Residential Care Homes. BMC Geriatrics, 20, Article No. 41. [Google Scholar] [CrossRef] [PubMed]
[25] Cole, S., Hua, C., Peng, S. and Wang, W. (2024) Exploring the Relationship of Leisure Travel with Loneliness, Depression, and Cognitive Function in Older Adults. International Journal of Environmental Research and Public Health, 21, Article 498. [Google Scholar] [CrossRef] [PubMed]
[26] McClintock, S.M., Minto, L., Denney, D.A., Bailey, K.C., Cullum, C.M. and Dotson, V.M. (2021) Clinical Neuropsychological Evaluation in Older Adults with Major Depressive Disorder. Current Psychiatry Reports, 23, Article No. 55. [Google Scholar] [CrossRef] [PubMed]
[27] Mougias, A., Christidi, F., Synetou, M., Kotrotsou, I., Valkimadi, P. and Politis, A. (2019) Differential Effect of Demographics, Processing Speed, and Depression on Cognitive Function in 755 Non-Demented Community-Dwelling Elderly Individuals. Cognitive and Behavioral Neurology, 32, 236-246. [Google Scholar] [CrossRef] [PubMed]
[28] Lee, J.E., Kahana, E., Kahana, B. and Zarit, S. (2020) The Role of Goal and Meaning in Life for Older Adults Facing Interpersonal Stress. Aging & Mental Health, 26, 149-158. [Google Scholar] [CrossRef] [PubMed]
[29] Ahn, S., Mathiason, M.A. and Yu, F. (2021) Longitudinal Cognitive Profiles by Anxiety and Depressive Symptoms in American Older Adults with Subjective Cognitive Decline. Journal of Nursing Scholarship, 53, 698-708. [Google Scholar] [CrossRef] [PubMed]
[30] Biasutti, M. and Mangiacotti, A. (2019) Music Training Improves Depressed Mood Symptoms in Elderly People: A Randomized Controlled Trial. The International Journal of Aging and Human Development, 92, 115-133. [Google Scholar] [CrossRef] [PubMed]
[31] Santos, A.P.B., Zajdenverg, L., Guimarães, H.C., Beato, R.G., de Almeida, M.A., Ritter, S.R.F., et al. (2021) Diabetes and Impaired Fasting Glucose in a Population-Based Sample of Individuals Aged 75+ Years: Associations with Cognition, Major Depressive Disorder, Functionality and Quality of Life—The Pietà Study. Neurological Sciences, 42, 3663-3671. [Google Scholar] [CrossRef] [PubMed]